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\"A guide to understanding the linkages that have been found between climate change and the growing severity and frequency of extreme weather events such as hurricanes, floods, droughts, and wildfires around the world\"-- Provided by publisher.

SignificanceThe Earth has warmed by 1.2 ± 0.1 °C since the preindustrial era. The most common metric to measure the ongoing global warming is surface air temperature since it has long and reliable observational records. However, surface air temperature alone does not fully describe the nature of global warming and its impact on climate and weather extremes. Here we show that surface equivalent potential temperature, which combines the surface air temperature and humidity, is a more comprehensive metric not only for the global warming but also for its impact on climate and weather extremes including tropical deep convection and extreme heat waves. We recommend that it should be used more widely in future climate change studies. Trends in surface air temperature (SAT) are a common metric for global warming. Using observations and observationally driven models, we show that a more comprehensive metric for global warming and weather extremes is the trend in surface equivalent potential temperature (Thetae_sfc) since it also accounts for the increase in atmospheric humidity and latent energy. From 1980 to 2019, while SAT increased by 0.79°C, Thetae_sfc increased by 1.48°C globally and as much as 4°C in the tropics. The increase in water vapor is responsible for the factor of 2 difference between SAT and Thetae_sfc trends. Thetae_sfc increased more uniformly (than SAT) between the midlatitudes of the southern hemisphere and the northern hemisphere, revealing the global nature of the heating added by greenhouse gases (GHGs). Trends in heat extremes and extreme precipitation are correlated strongly with the global/tropical trends in Thetae_sfc. The tropical amplification of Thetae_sfc is as large as the arctic amplification of SAT, accounting for the observed global positive trends in deep convection and a 20% increase in heat extremes. With unchecked GHG emissions, while SAT warming can reach 4.8°C by 2100, the global mean Thetae_sfc can increase by as much as 12°C, with corresponding increases of 12°C (median) to 24°C (5% of grid points) in land surface temperature extremes, a 14- to 30-fold increase in frequency of heat extremes, a 40% increase in the energy available for tropical deep convection, and an up to 60% increase in extreme precipitation. Description

Globally, 2023 was the warmest observed year on record since at least 1850 and, according to proxy evidence, possibly of the past 100 000 years. As in recent years, the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world. Here, we provide an overview of those of 2023, with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change. We also highlight emerging features associated with some of these extreme events. Hot extremes are occurring earlier in the year, and increasingly simultaneously in differing parts of the world (e.g., the concurrent hot extremes in the Northern Hemisphere in July 2023). Intense cyclones are exacerbating precipitation extremes (e.g., the North China flooding in July and the Libya flooding in September). Droughts in some regions (e.g., California and the Horn of Africa) have transitioned into flood conditions. Climate extremes also show increasing interactions with ecosystems via wildfires (e.g., those in Hawaii in August and in Canada from spring to autumn 2023) and sandstorms (e.g., those in Mongolia in April 2023). Finally, we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

Extreme weather disasters (EWDs) can jeopardize domestic food supply and disrupt commodity markets. However, historical impacts on European crop production associated with droughts, heatwaves, floods, and cold waves are not well understood—especially in view of potential adverse trends in the severity of impacts due to climate change. Here, we combine observational agricultural data (FAOSTAT) with an extreme weather disaster database (EM-DAT) between 1961 and 2018 to evaluate European crop production responses to EWD. Using a compositing approach (superposed epoch analysis), we show that historical droughts and heatwaves reduced European cereal yields on average by 9% and 7.3%, respectively, associated with a wide range of responses (inter-quartile range +2% to −23%; +2% to −17%). Non-cereal yields declined by 3.8% and 3.1% during the same set of events. Cold waves led to cereal and non-cereal yield declines by 1.3% and 2.6%, while flood impacts were marginal and not statistically significant. Production losses are largely driven by yield declines, with no significant changes in harvested area. While all four event frequencies significantly increased over time, the severity of heatwave and drought impacts on crop production roughly tripled over the last 50 years, from −2.2% (1964–1990) to −7.3% (1991–2015). Drought-related cereal production losses are shown to intensify by more than 3% yr−1. Both the trend in frequency and severity can possibly be explained by changes in the vulnerability of the exposed system and underlying climate change impacts.

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings. Key Points A data‐driven extreme weather prediction framework based on analog forecasting and deep learning pattern‐recognition methods is proposed Extreme surface temperature events over North America are skillfully predicted using only midtropospheric large‐scale circulation patterns More advanced deep learning methods are found to yield better forecasts, encouraging novel methods tailored for climate/weather data

Investigations into the role of anthropogenic climate change in extreme weather events are now starting to extend into analysis of anthropogenic impacts on non-climate (e.g. socio-economic) systems. However, care needs to be taken when making this extension, because methodological choices regarding extreme weather attribution can become crucial when considering the events’ impacts. The fraction of attributable risk (FAR) method, useful in extreme weather attribution research, has a very specific interpretation concerning a class of events, and there is potential to misinterpret results from weather event analyses as being applicable to specific events and their impact outcomes. Using two case studies of meteorological extremes and their impacts, we argue that FAR is not generally appropriate when estimating the magnitude of the anthropogenic signal behind a specific impact. Attribution assessments on impacts should always be carried out in addition to assessment of the associated meteorological event, since it cannot be assumed that the anthropogenic signal behind the weather is equivalent to the signal behind the impact because of lags and nonlinearities in the processes through which the impact system reacts to weather. Whilst there are situations where employing FAR to understand the climate change signal behind a class of impacts is useful (e.g. ‘system breaking’ events), more useful results will generally be produced if attribution questions on specific impacts are reframed to focus on changes in the impact return value and magnitude across large samples of factual and counterfactual climate model and impact simulations. We advocate for constant interdisciplinary collaboration as essential for effective and robust impact attribution assessments.

Transportation is affected by weather and extreme weather events, and there is evidence that heatwaves, heavy precipitation, storms, wildfires, and floods increasingly affect transport infrastructures, operations, and travel behavior. Climate change is expected to reinforce this trend, as mean weather parameters change, and the frequency and intensity of extreme events increases. This paper summarizes interrelationships of weather and transport for different transport modes from both supply and demand side perspectives on the basis of a literature review. To further explore the complexity of these interrelationships, it also evaluates news items (n = 839) in a sample of global media news outlets covering the world and population-dense world regions. Results confirm that extreme events have become disruptive of transport systems at the micro and macro scale, also affecting transport behavior. There are implications for environment, economy, technology, health, and society. Interrelationships are illustrated and discussed: Climatic impact drivers can be expected to increase transport vulnerabilities and risks, and have relevance for transport planning and adaptation.

Australian fire weather shows spatiotemporal variability on interannual and multi-decadal time scales. We investigate the climate factors that drive this variability using 39 station-based historical time series of the seasonal 90th-percentile of the McArthur Forest Fire Danger Index (FFDI) extending from 1973 through 2017. Using correlation analyses, we examine the relationship of these time series to the El Niño Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD), considering both concurrent and time-lagged relationships. Additionally, longer term behaviour of the time series using linear trend analysis is discussed in the context of the climate drivers, Interdecadal Pacific Oscillation (IPO) and anthropogenic climate change. The results show that ENSO is the main driver for interannual variability of fire weather, as defined by FFDI in this study, for most of Australia. In general, El Niño-like conditions lead to more extreme fire weather, with this effect stronger in eastern Australia. However, there are significant regional variations to this general rule. In NSW, particularly along the central coast, negative SAM is a primary influence for elevated fire weather in late-winter and spring. In the southeast (VIC and TAS), the El Niño-like impact is exacerbated when positive IOD conditions are simultaneously observed. The spring conditions are key, and strongly influence what is observed during the following summer. On longer time scales (45 years), linear trends are upward at most stations; this trend is strongest in the southeast and during the spring. The positive trends are not driven by the trends in the climate drivers and they are not consistent with hypothesized impacts of the IPO, either before or after its late-1990s shift to the cold phase. We propose that anthropogenic climate change is the primary driver of the trend, through both higher mean temperatures and potentially through associated shifts in large-scale rainfall patterns. Variations from interannual factors are generally larger in magnitude than the trend effects observed to date.

Future hydrological extremes, such as floods and droughts, may pose serious threats for the livelihoods in the upstream domains of the Indus, Ganges, Brahmaputra. For this reason, the impacts of climate change on future hydrological extremes is investigated in these river basins. We use a fully-distributed cryospheric-hydrological model to simulate current and future hydrological fluxes and force the model with an ensemble of 8 downscaled General Circulation Models (GCMs) that are selected from the RCP4.5 and RCP8.5 scenarios. The model is calibrated on observed daily discharge and geodetic mass balances. The climate forcing and the outputs of the hydrological model are used to evaluate future changes in climatic extremes, and hydrological extremes by focusing on high and low flows. The outcomes show an increase in the magnitude of climatic means and extremes towards the end of the 21st century where climatic extremes tend to increase stronger than climatic means. Future mean discharge and high flow conditions will very likely increase. These increases might mainly be the result of increasing precipitation extremes. To some extent temperature extremes might also contribute to increasing discharge extremes, although this is highly dependent on magnitude of change in temperature extremes. Low flow conditions may occur less frequently, although the uncertainties in low flow projections can be high. The results of this study may contribute to improved understanding on the implications of climate change for the occurrence of future hydrological extremes in the Hindu Kush-Himalayan region.

Observations unequivocally show that Iran has been rapidly warming over recent decades, which in sequence has triggered a wide range of climatic impacts. Meteorological records of several ground stations across Iran with daily temporal resolution for the period 1951–2013 were analyzed to investigate the climate change and its impact on some weather extremes. Iran has warmed by nearly 1.3 ∘C during the period 1951–2013 (+0.2 ∘C per decade), with an increase of the minimum temperature at a rate two times that of the maximum. Consequently, an increase in the frequency of heat extremes and a decrease in the frequency of cold extremes have been observed. The annual precipitation has decreased by 8 mm per decade, causing an expansion of Iran’s dry zones. Previous studies have pointed out that warming is generally associated with more frequent heavy precipitation because a warmer air can hold more moisture. Nevertheless, warming in Iran has been associated with more frequent light precipitation, but less frequent moderate, heavy and extremely heavy precipitation. This is because in the subtropical dry zones, a longer time is required to recharge the atmosphere with water vapour in a warmer climate, causing more water vapour to be transported from the subtropics to high latitudes before precipitations forms. In addition, the altitude of the condensation level increases in a warmer climate in subtropical regions, causing an overall decrease of precipitation. We argue that changing in the frequency of heavy precipitation in response to warming varies depending on the geographical location. Warming over the dry subtropical regions is associated with a decrease in the frequency of heavy precipitation, while an increase is expected over both subpolar and tropical regions. The warmer climate has also led to the increase in the frequency of both thunderstorms (driven by convective heating) and dust events over Iran.